What can be done to correct for sampling bias introduced from (noisy) training data while training a DNN?
I feel like from the information your giving (some sort of biased data) you can't get an answer as robust as you'd like (what algorithmic changes can be made).
In general, the reason these methods like DNN's work is that they learn from the data. What you train it to do is what it is capable of, and there's little one can do to 'balance' it to classes of data it just never sees. It's like training someone to do algebra then giving them a trigonometry test. It's all math, sure, but you just never can expect much without the proper learning.
That being said, you should perhaps look at other methods to work with this data, or to approach the problem. Given that you cannot collect unbiased data and that you can't explain more due to confidentiality, I really doubt anyone here can help you that much.
I can at the most point you to this article : &quot;Classification on Data with Biased Class Distribution&quot;.
And suggest that perhaps your current approach may not be the most appropriate given the unfortunate circumstances.